[SPARK-32310][ML][PySpark] ML params default value parity in classification, regression, clustering and fpm #29112
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Test build #125861 has finished for PR 29112 at commit
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So in theory this shouldn't change behavior, or if it does, it's fixing an incompatibility that's likely more a bug than anything right? |
Yes |
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"classification, regression, clustering and fpm" instead of "part 1" in the title? |
huaxingao
changed the title
| @@ -68,6 +68,12 @@ private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter | |||
| "The minimum number of points (if >= 1.0) or the minimum proportion " + | |||
| "of points (if < 1.0) of a divisible cluster.", ParamValidators.gt(0.0)) | |||
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| setDefault( | |||
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total nit: make these params in single line, like above places
| @@ -85,7 +85,6 @@ class FMClassifier @Since("3.0.0") ( | |||
| */ | |||
| @Since("3.0.0") | |||
| def setFactorSize(value: Int): this.type = set(factorSize, value) | |||
| setDefault(factorSize -> 8) | |||
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Where do the default params of FMClassifier move?
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I move setDefault for these params to FactorizationMachinesParams in FMRegressor when I fixed solver last time
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@srowen If it's OK with you, I will merge after test passes so I can finish the 2nd part. I will merge to both 3.0.1 and master. |
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Test build #125992 has finished for PR 29112 at commit
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Merged to mater. Thank you all for reviewing! |
What changes were proposed in this pull request?
set params default values in trait ...Params in both Scala and Python.
I will do this in two PRs. I will change classification, regression, clustering and fpm in this PR. Will change the rest in another PR.
Why are the changes needed?
Make ML has the same default param values between estimator and its corresponding transformer, and also between Scala and Python.
Does this PR introduce any user-facing change?
No
How was this patch tested?
Existing tests